Use CTRL/CMD + Shift + k to preview your markdown.

Above is the YAML (Yet Another Markup Language) header. It is a human-readable data serialization language. It sets some options for your markdown and gives you a nicely formatted preamble. It is currently set to some of my preferred settings, but feel free to play around with this and make it your own.

We have it set here to output as html, but you can just as easily produce PDF or Word documents. There are a bunch of built-in themes that you can explore here. I quite like the readable theme.

Below is a code chunk. You will notice that this one does not appear in the rendered document - this is because it is the setup chunk, and it has the option include=FALSE set. We use this to set options for chunk behavior, as well as loading packages and such.

1 A Section Header

The # above makes a header. A single # is the largest header, and extra #s are smaller headers.

1.1 Smaller Header

This header is automatically numbered because of the YAML settings and the the double #.

2 Code Chunks

Here we will explore some proper code chunks. You can use CTRL/CMD + ALT + I to create a new chunk. If we want to run code, but not show it, we can use echo = FALSE in the chunk options.

Otherwise, our code chunk will be visible. Let’s show off our example regression from the FSCI paper.

lm <- lm(normvalue ~ year + FSCI_region, data = df)
summary(lm)
## 
## Call:
## lm(formula = normvalue ~ year + FSCI_region, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -31.370  -8.881  -3.519   6.425  72.022 
## 
## Coefficients:
##                                            Estimate Std. Error t value Pr(>|t|)
## (Intercept)                               802.36120   99.04397   8.101 8.45e-16
## year                                       -0.39300    0.04928  -7.975 2.29e-15
## FSCI_regionEastern Asia                    11.45318    2.49413   4.592 4.61e-06
## FSCI_regionLatin America & Caribbean        0.24044    1.75020   0.137  0.89074
## FSCI_regionNorthern Africa & Western Asia  -3.01052    1.81558  -1.658  0.09741
## FSCI_regionNorthern America and Europe     -7.85822    2.04028  -3.852  0.00012
## FSCI_regionOceania                         -0.14376    2.09696  -0.069  0.94535
## FSCI_regionSouth-eastern Asia               2.58542    1.95749   1.321  0.18669
## FSCI_regionSouthern Asia                    4.86597    2.03578   2.390  0.01691
## FSCI_regionSub-Saharan Africa              15.54695    1.69547   9.170  < 2e-16
##                                              
## (Intercept)                               ***
## year                                      ***
## FSCI_regionEastern Asia                   ***
## FSCI_regionLatin America & Caribbean         
## FSCI_regionNorthern Africa & Western Asia .  
## FSCI_regionNorthern America and Europe    ***
## FSCI_regionOceania                           
## FSCI_regionSouth-eastern Asia                
## FSCI_regionSouthern Asia                  *  
## FSCI_regionSub-Saharan Africa             ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14.93 on 2484 degrees of freedom
## Multiple R-squared:  0.2395, Adjusted R-squared:  0.2368 
## F-statistic: 86.93 on 9 and 2484 DF,  p-value: < 2.2e-16

This shows our code and output, but it is not terribly clean looking.

3 Clean Regression Outputs

To get a cleaner regression output, we can convert our regression output to a data frame, then use knitr::kable() to create a nice looking table.

lm_df <- broom::tidy(lm)
knitr::kable(lm_df)
term estimate std.error statistic p.value
(Intercept) 802.3611950 99.0439650 8.1010609 0.0000000
year -0.3930000 0.0492762 -7.9754489 0.0000000
FSCI_regionEastern Asia 11.4531769 2.4941272 4.5920580 0.0000046
FSCI_regionLatin America & Caribbean 0.2404370 1.7502015 0.1373768 0.8907441
FSCI_regionNorthern Africa & Western Asia -3.0105244 1.8155793 -1.6581619 0.0974110
FSCI_regionNorthern America and Europe -7.8582226 2.0402820 -3.8515374 0.0001203
FSCI_regionOceania -0.1437598 2.0969580 -0.0685563 0.9453483
FSCI_regionSouth-eastern Asia 2.5854171 1.9574857 1.3207847 0.1866948
FSCI_regionSouthern Asia 4.8659655 2.0357824 2.3902188 0.0169124
FSCI_regionSub-Saharan Africa 15.5469469 1.6954749 9.1696708 0.0000000

Extra steps to get the column names capitalized and the numbers rounded:

lm_df_cleaner <- lm_df %>% 
  mutate(across(where(is.numeric), ~ round(.x, 3))) %>% 
  setNames(c(snakecase::to_title_case(names(.))))
knitr::kable(lm_df_cleaner)
Term Estimate Std Error Statistic P Value
(Intercept) 802.361 99.044 8.101 0.000
year -0.393 0.049 -7.975 0.000
FSCI_regionEastern Asia 11.453 2.494 4.592 0.000
FSCI_regionLatin America & Caribbean 0.240 1.750 0.137 0.891
FSCI_regionNorthern Africa & Western Asia -3.011 1.816 -1.658 0.097
FSCI_regionNorthern America and Europe -7.858 2.040 -3.852 0.000
FSCI_regionOceania -0.144 2.097 -0.069 0.945
FSCI_regionSouth-eastern Asia 2.585 1.957 1.321 0.187
FSCI_regionSouthern Asia 4.866 2.036 2.390 0.017
FSCI_regionSub-Saharan Africa 15.547 1.695 9.170 0.000

For a very clean plot with less work, try the sjPlot package:

sjPlot::tab_model(
  lm, 
  p.style = 'stars', 
  digits = 2,
  show.se = TRUE
)
  normvalue
Predictors Estimates std. Error CI
(Intercept) 802.36 *** 99.04 608.14 – 996.58
year -0.39 *** 0.05 -0.49 – -0.30
FSCI region [Eastern
Asia] 		11.45 *** 2.49 6.56 – 16.34
FSCI region [Latin
America & Caribbean] 		0.24 1.75 -3.19 – 3.67
FSCI region [Northern
Africa & Western Asia] 		-3.01 1.82 -6.57 – 0.55
FSCI region [Northern
America and Europe] 		-7.86 *** 2.04 -11.86 – -3.86
FSCI region [Oceania] -0.14 2.10 -4.26 – 3.97
FSCI region
[South-eastern Asia] 		2.59 1.96 -1.25 – 6.42
FSCI region [Southern
Asia] 		4.87 * 2.04 0.87 – 8.86
FSCI region [Sub-Saharan
Africa] 		15.55 *** 1.70 12.22 – 18.87
Observations 2494
R2 / R2 adjusted 0.240 / 0.237
  • p<0.05   ** p<0.01   *** p<0.001

Check out the documentation here. This is where you really learn how to use a package. It is written by the author, with abundant vignettes and examples.

4 Interactive Table

We’ve already seen how to make tables above. For static tables, knitr::kable() is a good choice. The kableExtra package is also a great extension to knitr, giving you tons of options for customization. See the docs for examples.

For interactive tables, the DT package is a great option, but my personal favorite is reactable. The documentation is excellent, so check it out if you’re interested.

Note that we are setting echo=FALSE here, so the code chunk will not show up.

5 Plots

We haven’t really covered plots, but you really just throw your code in the chunk and it will appear.

gapminder %>% 
  filter(year == 2007) %>% 
  ggplot(aes(x = gdpPercap, y = lifeExp, color = continent, size = pop)) +
  geom_point() +
  theme_classic() +
  labs(
    x = 'GDP per Capita',
    y = 'Life Expectancy',
    title = 'Life Expectancy against GDP per Capita'
  )

What about an interactive plot?

plot <- gapminder %>% 
  filter(year == 2007) %>% 
  ggplot(aes(
    x = gdpPercap, 
    y = lifeExp, 
    color = continent,
    size = pop,
    text = paste0(
      'Country: ', country, '\n',
      'Continent: ', continent, '\n',
      'Life Exp: ', lifeExp, '\n',
      'Population: ', pop
    )
  )) +
  geom_point() +
  theme_classic() +
  labs(
    x = 'GDP per Capita',
    y = 'Life Expectancy',
    title = 'Life Expectancy against GDP per Capita'
  )

ggplotly(plot, tooltip = 'text')